Civil Service Statistics data browser (2023)

Data preview: All civil servants / Region_ITL1 / Region_ITL3 / Sex / Region_ITL2

Status Year Region_ITL1 Region_ITL3 Sex Region_ITL2 Headcount FTE Mean_salary Median_salary
In post 2023 East Midlands (England) Derby Female Derbyshire and Nottinghamshire 740 630 27020 25300
In post 2023 East Midlands (England) Derby Male Derbyshire and Nottinghamshire 395 380 31270 28120
In post 2023 East Midlands (England) East Derbyshire Female Derbyshire and Nottinghamshire 270 230 28280 28120
In post 2023 East Midlands (England) East Derbyshire Male Derbyshire and Nottinghamshire 115 115 33220 28120
In post 2023 East Midlands (England) Leicester Female Leicestershire, Rutland and Northamptonshire 1445 1275 28620 27450
In post 2023 East Midlands (England) Leicester Male Leicestershire, Rutland and Northamptonshire 925 885 29000 28120
In post 2023 East Midlands (England) Leicestershire CC and Rutland Female Leicestershire, Rutland and Northamptonshire 1120 1005 29780 28120
In post 2023 East Midlands (England) Leicestershire CC and Rutland Male Leicestershire, Rutland and Northamptonshire 970 940 33180 31510
In post 2023 East Midlands (England) Lincolnshire CC Female Lincolnshire 1625 1490 29400 27740
In post 2023 East Midlands (England) Lincolnshire CC Male Lincolnshire 1460 1405 32240 30400
In post 2023 East Midlands (England) North Northamptonshire Female Leicestershire, Rutland and Northamptonshire 435 380 27230 24570
In post 2023 East Midlands (England) North Northamptonshire Male Leicestershire, Rutland and Northamptonshire 170 165 28900 28120
In post 2023 East Midlands (England) North Nottinghamshire Female Derbyshire and Nottinghamshire 625 570 31610 28120
In post 2023 East Midlands (England) North Nottinghamshire Male Derbyshire and Nottinghamshire 520 500 32570 28880
In post 2023 East Midlands (England) Nottingham Female Derbyshire and Nottinghamshire 5230 4795 33430 28840
In post 2023 East Midlands (England) Nottingham Male Derbyshire and Nottinghamshire 4175 4050 34450 31510
In post 2023 East Midlands (England) South Nottinghamshire Female Derbyshire and Nottinghamshire 575 535 34790 30360
In post 2023 East Midlands (England) South Nottinghamshire Male Derbyshire and Nottinghamshire 485 470 34250 30440
In post 2023 East Midlands (England) South and West Derbyshire Female Derbyshire and Nottinghamshire 715 640 34000 29470
In post 2023 East Midlands (England) South and West Derbyshire Male Derbyshire and Nottinghamshire 510 485 38430 32920
Note: Data has been truncated to 20 rows, please download the data to view the remaining rows

Download the data

About: The Civil Service Statistics data browser is a pilot project by Cabinet Office to provide access to more detailed data on the Civil Service workforce from the Annual Civil Service Employment Survey. We welcome feedback or comments on this project, which can be addressed to civilservicestatistics@cabinetoffice.gov.uk

Notes: Summary figures are suppressed when information relates to less than 5 civil servants for FTE or Headcount, and less than 10 civil servants for median and mean salary (shown as [c]). Zero responses and salaries for less than 30 civil servants have been suppressed for GPDR special category data. FTE figures are not shown for entrants or leavers due to data quality concerns for these groups. Figures are rounded to the nearest 5, or £10 as appropriate.

Data source: All figures are aggregated from the Cabinet Office Annual Civil Service Employment Survey collection.

Version: Generated on 2023-07-26, with GIT d545f65.

Data column Description
Status Employment status of the civil servants.
In post - includes staff that were in post on the reference date (31 March).
New entrant CS - includes new entrants to the Civil Service over the year (1 April to 31 March).
Leaver CS - includes leavers from the Civil Service over the year (1 April to 31 March). This includes employees who have an Unknown leaving cause.
Leaver Dept. - includes leavers from the department over the year (1 April to 31 March), who did not leave the Civil Service.
Four organisations do not report when their employees first entered the Civil Service and so entrants data for these organisations is not available . These are as follows: Foreign Commonwealth and Development Office (excl. agencies), Foreign Commonwealth and Development Office Services, Scottish Forestry and Forest and Land Scotland. A further three organisations also could not provide entrants data in 2021. These are as follows: Department for International Development, Foreign and Commonwealth Office (excl. agencies) and Royal Fleet Auxiliary.
Year Year of data collection (as at 31 March).
Region_ITL1 Workplace postcode data are used to derive geographical information using the International Territorial Level (ITL) classification standard.
Following the UK’s withdrawal from the EU, a new UK-managed international statistical geography - International Territorial Levels (ITL) - was introduced from 1st January 2021, replacing the former NUTS classification. They align with international standards, enabling comparability both over time and internationally. To ensure continued alignment, the ITLs mirror the NUTS system. They also follow a similar review timetable - every three years.
ITL 1 divides into Wales, Scotland, Northern Ireland, and the 9 statistical regions of England.
Region_ITL2 Workplace postcode data are used to derive geographical information using the International Territorial Level (ITL) classification standard.
Following the UK’s withdrawal from the EU, a new UK-managed international statistical geography - International Territorial Levels (ITL) - was introduced from 1st January 2021, replacing the former NUTS classification. They align with international standards, enabling comparability both over time and internationally. To ensure continued alignment, the ITLs mirror the NUTS system. They also follow a similar review timetable - every three years.
ITL 2 divides into Northern Ireland, counties in England (most grouped), groups of districts in Greater London, groups of unitary authorities in Wales, groups of council areas in Scotland.
Region_ITL3 Workplace postcode data are used to derive geographical information using the International Territorial Level (ITL) classification standard.
Following the UK’s withdrawal from the EU, a new UK-managed international statistical geography - International Territorial Levels (ITL) - was introduced from 1st January 2021, replacing the former NUTS classification. They align with international standards, enabling comparability both over time and internationally. To ensure continued alignment, the ITLs mirror the NUTS system. They also follow a similar review timetable - every three years.
ITL 3 divides into counties, unitary authorities, or districts in England (some grouped), groups of unitary authorities in Wales, groups of council areas in Scotland, groups of districts in Northern Ireland.
Sex Self reported sex.
"Unknown" accounts for employees who were recorded with an unknown sex.
Headcount Total number of civil servants (rounded to nearest 5).
FTE Total full-time equivalent (FTE) employment numbers (rounded to nearest 5).
FTE figures are not shown for entrants or leavers due to data quality concerns for these groups.
Mean_salary Average salary (mean, rounded to nearest £10). For part-time employees, salaries represent the full-time equivalent earnings, while for full-time employees they are the actual annual gross salaries.
These figures should be interpreted with caution when the total number of employees in a group is small, as they will tend to show more variability than larger groups (i.e. may be much higher or lower than can be explained by the data shown).
Median_salary Median salary (rounded to nearest £10). For part-time employees, salaries represent the full-time equivalent earnings, while for full-time employees they are the actual annual gross salaries.
These figures should be interpreted with caution when the total number of employees in a group is small, as they will tend to show more variability than larger groups (i.e. may be much higher or lower than can be explained by the data shown).